opencv_contrib VS NumPy

I managed to use the cv2.ximgproc.ContourFitting in cpp instead of python with the help of the demo: https://github.com/opencv/opencv_contrib/blob/3.4.0/modules/ximgproc/samples/fourier_descriptors_demo.cpp

wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.1.0.zip

The issue has been reported here: https://github.com/opencv/opencv_contrib/issues/3390

FROM debian:bullseye-slim AS build-native-env ARG TARGETPLATFORM ENV DEBIAN_FRONTEND=noninteractive # 4.5.5: released 25 Dec 2021 ENV OPENCV_VERSION=4.5.5 # 4.5.3.20211228: released 28 Dec 2021 ENV OPENCVSHARP_VERSION=4.5.3.20211228 WORKDIR / # install dependencies required for building OpenCV and OpenCvSharpExtern RUN apt-get update && apt-get -y install --no-install-recommends \ # details omitted \ libgdiplus # Get OpenCV and opencv-contrib sources using the specified release. RUN wget https://github.com/opencv/opencv/archive/${OPENCV_VERSION}.zip && \ unzip ${OPENCV_VERSION}.zip && \ rm ${OPENCV_VERSION}.zip && \ mv opencv-${OPENCV_VERSION} opencv RUN wget https://github.com/opencv/opencv_contrib/archive/${OPENCV_VERSION}.zip && \ unzip ${OPENCV_VERSION}.zip && \ rm ${OPENCV_VERSION}.zip && \ mv opencv_contrib-${OPENCV_VERSION} opencv_contrib # configure and build OpenCV optionally specifying architecture related cmake options. RUN if [ "$TARGETPLATFORM" = "linux/amd64" ]; then \ ADDITIONAL_FLAGS='' ; \ elif [ "$TARGETPLATFORM" = "linux/arm64" ]; then \ ADDITIONAL_FLAGS='-D ENABLE_NEON=ON -D CPU_BASELINE=NEON ' ; \ elif [ "$TARGETPLATFORM" = "linux/arm/v7" ]; then \ ADDITIONAL_FLAGS='-D CPU_BASELINE=NEON -D ENABLE_NEON=ON ' ; \ fi && cd opencv && mkdir build && cd build && \ cmake $ADDITIONAL_FLAGS \ # additional flags omitted for clarity \ && make -j$(nproc) \ && make install \ && ldconfig # Download OpenCvSharp to build OpenCvSharpExtern native library RUN git clone https://github.com/shimat/opencvsharp.git RUN cd opencvsharp && git fetch --all --tags --prune && git checkout ${OPENCVSHARP_VERSION} WORKDIR /opencvsharp/src RUN mkdir /opencvsharp/make \ && cd /opencvsharp/make \ && cmake -D CMAKE_INSTALL_PREFIX=/opencvsharp/make /opencvsharp/src \ && make -j$(nproc) \ && make install \ && cp /opencvsharp/make/OpenCvSharpExtern/libOpenCvSharpExtern.so /usr/lib/ \ && ldconfig # Copy the library and dependencies to /artifacts (to be used by images consuming this build) # cpld.sh will copy the library we specify (./libOpenCvSharpExtern.so) and any dependencies # to the /artifacts directory. This is useful for sharing the library with other images # consuming this build. # credits: Hemanth.HM -> https://h3manth.com/content/copying-shared-library-dependencies WORKDIR /opencvsharp/make/OpenCvSharpExtern COPY cpld.sh . RUN chmod +x cpld.sh && \ mkdir /artifacts && \ ./cpld.sh ./libOpenCvSharpExtern.so /artifacts/ RUN cp ./libOpenCvSharpExtern.so /artifacts/ # Publish the artefacts using a clean image FROM debian:bullseye-slim AS final RUN mkdir /artifacts COPY --from=build-native-env /artifacts/ /artifacts WORKDIR /artifacts

As the title implies, I'm looking for the simplest way to detect race/ethnicity in a photo (or a frame by frame video, shouldn't big a big difference) using OpenCV (I'm using the opencv-contrib build) and C++. I'm a complete beginner.

git clone https://github.com/opencv/opencv.git git clone https://github.com/opencv/opencv_contrib.git mkdir ~/opencv/build cd ~/opencv/build

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]